Exploring the Human Virome: Composition, Dynamics, and Implications for Health and Disease

Ten computational challenges in human virome studies

In recent years, substantial advancements have been achieved in understanding the diversity of the human virome and its intricate roles in human health and diseases. Despite this progress, the field of human virome research remains nascent, primarily hindered by the absence of effective methods, particularly in the domain of computational tools. This perspective systematically outlines ten computational challenges spanning various types of virome studies. These challenges arise due to the vast diversity of viromes, the absence of a universal marker gene in viral genomes, the low abundance of virus populations, the remote or minimal homology of viral proteins to known proteins, and the highly dynamic and heterogeneous nature of viromes. For each computational challenge, we discuss the underlying reasons, current research progress, and potential solutions. The resolution of these challenges necessitates ongoing collaboration among computational scientists, virologists, and multidisciplinary experts. In essence, this perspective serves as a comprehensive guide for directing computational efforts in human virome studies.

Genetic evidence strengthens the bidirectional connection between gut microbiota and Shigella infection: insights from a two-sample Mendelian randomization study

Background

In recent investigations, substantial strides have been made in the precise modulation of the gut microbiota to prevent and treat a myriad of diseases. Simultaneously, the pressing issue of widespread antibiotic resistance and multidrug resistance resulting from Shigella infections demands urgent attention. Several studies suggest that the antagonistic influence of the gut microbiota could serve as a novel avenue for impeding the colonization of pathogenic microorganisms or treating Shigella infections. However, conventional research methodologies encounter inherent challenges in identifying antagonistic microbial agents against Shigella, necessitating a comprehensive and in-depth analysis of the causal relationship between Shigella infections and the gut microbiota.

Materials and methods

Utilizing the aggregated summary statistics from Genome-Wide Association Studies (GWAS), we conducted Mendelian Randomization (MR) analyses encompassing 18,340 participants to explore the interplay between the gut microbiota and Shigella infections. This investigation also involved 83 cases of Shigella infection patients and 336,396 control subjects. In the positive strand of our findings, we initially performed a preliminary analysis using the Inverse Variance Weighting (IVW) method. Subsequently, we undertook sensitivity analyses to assess the robustness of the results, addressing confounding factors' influence. This involved employing the Leave-One-Out method and scrutinizing funnel plots to ensure the reliability of the MR analysis outcomes. Conclusively, a reverse MR analysis was carried out, employing the Wald ratio method due to the exposure of individual Single Nucleotide Polymorphisms (SNPs). This was undertaken to explore the plausible associations between Shigella infections and genetically predicted compositions of the gut microbiota.

Results

In this study, we employed 2,818 SNPs associated with 211 species of gut microbiota as instrumental variables (IVs). Through IVW analysis, our positive MR findings revealed a significant negative correlation between the occurrence of Shigella infections and the phylum Tenericutes (OR: 0.18, 95% CI: 0.04-0.74, p = 0.02), class Mollicutes (OR: 0.18, 95% CI: 0.04-0.74, p = 0.02), genus Intestinimonas (OR: 0.16, 95% CI: 0.04-0.63, p = 0.01), genus Gordonibacter (OR: 0.39, 95% CI: 0.16-0.93, p = 0.03), and genus Butyrivibrio (OR: 0.44, 95% CI: 0.23-0.87, p = 0.02). Conversely, a positive correlation was observed between the occurrence of Shigella infections and genus Sutterella (OR: 10.16, 95% CI: 1.87-55.13, p = 0.01) and genus Alistipes (OR: 12.24, 95% CI: 1.71-87.34, p = 0.01). In sensitivity analyses, utilizing MR-Egger regression analysis and MR Pleiotropy Residual Sum and Outlier (MR-PRESSO) detection, all outcomes demonstrated robust stability. Simultaneously, in the reverse MR analysis, Shigella infections resulted in an upregulation of four bacterial genera and a downregulation of three bacterial genera.

Conclusion

In summation, the MR analysis outcomes corroborate the presence of bidirectional causal relationships between the gut microbiota and Shigella infections. This study not only unveils novel perspectives for the prevention and treatment of Shigella infections but also furnishes fresh insights into the mechanistic underpinnings of how the gut microbiota contributes to the pathogenesis of Shigella infections. Consequently, the established dual causal association holds promise for advancing our understanding and addressing the complexities inherent in the interplay between the gut microbiota and Shigella infections, thereby paving the way for innovative therapeutic interventions and preventive strategies in the realm of Shigella-related diseases.

Gut microbiota and risk of endocarditis: a bidirectional Mendelian randomization study

Background

The associations between gut microbiota and cardiovascular disease have been reported in previous studies. However, the relationship between gut microbiota and endocarditis remains unclear.

Methods

A bidirectional Mendelian randomization (MR) study was performed to detect the association between gut microbiota and endocarditis. Inverse variance weighted (IVW) method was considered the main result. Simultaneously, heterogeneity and pleiotropy tests were conducted.

Results

Our study suggests that family Victivallaceae (p = 0.020), genus Eubacterium fissicatena group (p = 0.047), genus Escherichia Shigella (p = 0.024), genus Peptococcus (p = 0.028) and genus Sellimonas (p = 0.005) play protective roles in endocarditis. Two microbial taxa, including genus Blautia (p = 0.006) and genus Ruminococcus2 (p = 0.024) increase the risk of endocarditis. At the same time, endocarditis has a negative effect on genus Eubacterium fissicatena group (p = 0.048). Besides, no heterogeneity or pleiotropy was found in this study.

Conclusion

Our study emphasized the certain role of specific gut microbiota in patients with endocarditis and clarified the negative effect of endocarditis on gut microbiota.